Prompt Optical Emission from Gamma-ray Bursts with Non-single Timescale Variability of Central Engine Activities

The complete high-resolution lightcurves of Swift GRB 080319B present an opportunity for detailed temporal analysis of the prompt optical emission. With a two-component distribution of initial Lorentz factors, we simulate the dynamical process of the ejected shells from the central engine in the framework of the internal shock model. The emitted radiation are decomposed into different frequency ranges for a temporal correlation analysis between the lightcurves in different energy bands. The resulting prompt optical and gamma-ray emission show similar temporal profiles, both as a superposition of a slow variability component and a fast variability component, except that the gamma-ray lightcurve is much more variable than its optical counterpart. The variability features in the simulated lightcurves and the strong correlation with a time lag between the optical and gamma-ray emission are in good agreement with the observations of GRB 080319B. Our simulations suggest that the variations seen in the lightcurves stem from the temporal structure of the shells injected from the central engine of gamma-ray bursts. The future high temporal resolution observations of prompt optical emission from GRBs, e.g., by UFFO-Pathfinder and SVOM-GWAC, provide a useful tool to investigate the central engine activity.Comment: 12 pages, 6 figures, RAA accepte

Understanding Kernel Size in Blind Deconvolution

Most blind deconvolution methods usually pre-define a large kernel size to guarantee the support domain. Blur kernel estimation error is likely to be introduced, yielding severe artifacts in deblurring results. In this paper, we first theoretically and experimentally analyze the mechanism to estimation error in oversized kernel, and show that it holds even on blurry images without noises. Then to suppress this adverse effect, we propose a low rank-based regularization on blur kernel to exploit the structural information in degraded kernels, by which larger-kernel effect can be effectively suppressed. And we propose an efficient optimization algorithm to solve it. Experimental results on benchmark datasets show that the proposed method is comparable with the state-of-the-arts by accordingly setting proper kernel size, and performs much better in handling larger-size kernels quantitatively and qualitatively. The deblurring results on real-world blurry images further validate the effectiveness of the proposed method.Comment: Accepted by WACV 201